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CUTLASS 3.4.0 (#1286)

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* CUTLASS 3.4.0

* Update CHANGELOG.md

---------

Co-authored-by: Pradeep Ramani <prramani@nvidia.com>
This commit is contained in:
Pradeep Ramani
2023-12-29 12:21:31 -08:00
committed by GitHub
parent b7508e3379
commit 8236f30675
211 changed files with 11409 additions and 2763 deletions
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19
test/python/cutlass/gemm/gemm_testbed.py
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@@ -128,13 +128,22 @@ class GemmUniversalLauncher:
def uniform_init(self, shape, dtype, layout):
size = prod(shape)
if dtype.is_floating_point:
data = torch.ceil(torch.empty(size=(size,), dtype=dtype, device="cuda").uniform_(self.rand_min - 0.5, self.rand_max - 0.5))
# Initialize data in FP32 and call convert to the data type we desire.
# This is a workaround for the following error that occurs when attempting to
# call uniform_ on a tensor with torch.float8_e4m3fn data:
# RuntimeError: "check_uniform_bounds" not implemented for 'Float8_e4m3fn'
data = torch.ceil(
torch.empty(size=(size,), dtype=torch.float32, device="cuda").uniform_(
self.rand_min - 0.5, self.rand_max - 0.5)
).to(dtype)
else:
# PyTorch does not currently support integer-typed matrix multiplications on GPU.
# Fall back to CPU for integer type references.
data = torch.empty(size=(size,), dtype=dtype, device="cpu").random_(self.rand_min, self.rand_max + 1)
if dtype == torch.float64 or dtype == torch.float32:
is_fp8 = dtype == getattr(torch, "float8_e4m3fn", -1) or dtype == dtype == getattr(torch, "float8_e5m2", -1)
if dtype == torch.float64 or dtype == torch.float32 or is_fp8:
data = data.to("cpu")
data_ref = data.reshape(shape)
@@ -145,6 +154,12 @@ class GemmUniversalLauncher:
data_cutlass = data_ref.transpose(-1, -2).contiguous()
data_cutlass = data_cutlass.to("cuda")
# As of this writing, few operations in PyTorch are supported with FP8 data.
# Thus, we perform computation in FP32 for FP8 reference checks.
if is_fp8:
data_ref = data_ref.to(torch.float32)
return data_cutlass, data_ref
def reference(self, problem_size, tensor_A, tensor_B, tensor_C, alpha, beta):